Baah et al.[1]} pointed out that the conventional SFL techniques are susceptible to confounding bias, and they employed causal inference methodology to localize faults. A linear regression model was fitted for test outcomes with a coverage indicator for the target statement as the treatment and a coverage indicator for its forward control dependence predecessor as a covariate. Bai et al. presented two variants of a value-based causal statistical fault localization technique called NUMFL [2]}. This technique makes use of generalized propensity scores, which are used to achieve covariate balance. Although these causal inference based techniques adjust for confounding like UniVal, they employ parametric regression and thus lack the modeling flexbility of random forests. Elastic predicates (ESP) [3]}, which were presented by Gore et al., involve measuring how different, in standard deviations, an assigned variable's value is from its average value. This difference is used as a suspiciousness score. In contrast to UniVal, ESP does not control for confounding bias.
